Target and reference image frame detection based on automated intraluminal imaging and associated devices, systems, and methods

Abstract

An intraluminal imaging system is disclosed that includes an intraluminal imaging catheter or guidewire configured to obtain imaging data associated with a lumen of a patient when positioned within the lumen and a processor in communication with the intraluminal imaging catheter or guidewire. The processor is configured to generate a plurality of image frames using the imaging data, automatically measure anatomical features in the image frames, identify a target frame representing a region of interest, identify a proximal reference frame located proximal to the target frame, and identify a distal reference frame located distal to the target frame. The processor is further configured to output a single screen display that includes the proximal reference frame, the target frame, the distal reference frame, and a longitudinal representation of the lumen showing respective positions of the proximal reference frame, the target frame, and the distal reference frame.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to luminal imaging data obtained by a catheter or guidewire positioned within a body lumen of a patient. In particular, the present disclosure relates to automatic identification of a target frame of interest and a reference frame within an image data set based on luminal imaging data obtained by a catheter or guidewire. BACKGROUND

[0002] Various types of luminal (also referred to as intravascular) imaging systems are used in the diagnosis and treatment of disease. For example, intravascular ultrasound (IVUS) imaging is widely used in interventional cardiology as a diagnostic tool for visualizing blood vessels within a patient’s body. This can assist in assessing diseased blood vessels, such as arteries and veins, within the human body to determine treatment needs, optimize treatment, and / or assess the effectiveness of treatment.

[0003] In some cases, luminal imaging is performed with an IVUS catheter that includes one or more ultrasound transducers. The IVUS device can be delivered into a blood vessel and the IVUS catheter is guided to a region to be imaged. The transducers emit ultrasound energy and receive ultrasound echoes reflected from the blood vessel. The ultrasound echoes are processed to create an image of the blood vessel of interest.

[0004] The adoption of luminal imaging technology is diverse around the world, and in many places around the world it is underutilized relative to the clinical evidence and benefits it provides. One barrier to the use of luminal imaging is the manual selection of regions of interest. For example, some treatments (e.g., percutaneous coronary intervention or PCI) can involve placing a stent in a blood vessel in order to widen the blood vessel at a location, and proper placement and expansion of the stent can be very important for a favorable outcome. IVUS-guided stent placement can be associated with better outcomes than angiography-guided stent placement. However, proper placement of a stent involves manual selection of a target location (e.g., a target frame or range of target frames of an IVUS pullback sequence) where the lumen diameter or cross-sectional area is smallest, as well as proximal and distal reference locations (e.g., IVUS tomographic image frames proximal and distal to the target frame) where the lumen diameter or cross-sectional area is in a healthy or expected range. In some instances, the stent is placed so that it completely covers the target location, and so that its edges coincide with the proximal and distal reference locations. However, there is no standardized protocol for selecting the target of interest and potential references. This, along with barriers to image interpretation, is a barrier for novice and intermediate users when analyzing images acquired during an IVUS pullback sequence. SUMMARY

[0005] Systems, devices, and methods for displaying multiple intraluminal images, referred to hereinafter as an automatic frame identification system, are disclosed. The present disclosure describes an end-to-end algorithm to take frame-by-frame measurements from an IVUS pullback sequence and determine one or more target of interest (e.g., stenosis) and corresponding proximal and distal healthy reference frames to help reduce the time spent on manual analysis of pullback results and help guide selection of landing zones for a stent. In the case of a post-stent placement procedure, the minimum stent area is automatically indicated. Various aspects of the algorithm can be configurable to match individual preferences and experiences.

[0006] One general aspect of the automatic frame identification system can include an intraluminal imaging system including an intraluminal imaging catheter or guidewire configured to obtain imaging data associated with a lumen of a patient while positioned within the lumen, and a processor circuit in communication with the intraluminal imaging catheter or guidewire, the processor circuit configured to: generate a plurality of image frames using the imaging data; automatically measure an anatomical feature in the plurality of image frames in response to generating the plurality of image frames; identify, based on the automatically measured anatomical feature, from among the plurality of image frames: a target frame representing a region of interest, a proximal reference frame located proximal to the target frame, and a distal reference frame located distal to the target frame. The processor circuit can also be configured to output, to a display in communication with the processor circuit, a single screen display including: the proximal reference frame, the target frame, the distal reference frame, and a longitudinal representation of the lumen showing respective positions of the proximal reference frame, the target frame, and the distal reference frame.

[0007] Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods. A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination thereof installed on the system that in operation

[0008] Implementations can include one or more of the following features. The intraluminal imaging system, wherein the processor circuit identifying the target frame includes the processor circuit identifying an image frame that satisfies a first criterion associated with a plaque burden or cross-sectional area of the lumen. The intraluminal imaging system, wherein the processor circuit identifying the proximal reference frame includes the processor circuit identifying an image frame representing a proximal-most tissue of the lumen proximal to the target frame, the image frame satisfying a second criterion associated with a plaque burden or cross-sectional area of the lumen. The intraluminal imaging system, wherein the processor circuit identifying the distal reference frame includes the processor circuit identifying an image frame representing a proximal-most tissue of the lumen distal to the target frame, the image frame satisfying a third criterion associated with a plaque burden or cross-sectional area of the lumen. The intraluminal imaging system, wherein the processor circuit is configured to define a target region between a plurality of candidate target frames, wherein no reference frame is identified between the plurality of candidate target frames. The intraluminal imaging system, wherein the processor circuit identifying the target frame includes the processor circuit identifying an image frame that satisfies a fourth criterion associated with a plaque burden or cross-sectional area of the plurality of candidate target frames within the target region. The intraluminal imaging system, wherein the processor circuit identifying the target frame includes the processor circuit identifying an image frame representing a desired location to be expanded with a stent. The intraluminal imaging system, wherein the processor circuit identifying the proximal reference frame includes the processor circuit identifying an image frame representing a desired location of a proximal edge of the stent. The intraluminal imaging system, wherein the processor circuit identifying the distal reference frame includes the processor circuit identifying an image frame representing a desired location of a distal edge of the stent. The intraluminal imaging system, wherein the processor circuit identifying the target frame includes the processor circuit identifying an image frame having a smallest cross-sectional area within a stent positioned in the lumen. The intraluminal imaging system, wherein the processor circuit is configured to detect a proximal edge and a distal edge of the stent. The intraluminal imaging system, wherein the processor circuit identifying the proximal reference frame includes the processor circuit identifying an image frame representing the proximal edge of the stent. The intraluminal imaging system, wherein the processor circuit identifying the distal reference frame includes the processor circuit identifying an image frame representing the distal edge of the stent. Implementations of the described technology can include hardware, a method or process, or computer software on a computer-accessible medium.

[0009] One general aspect includes a method for intraluminal imaging, the method comprising: using a processor circuit in communication with an intraluminal imaging catheter or guidewire, generating a plurality of image frames using imaging data obtained by the intraluminal imaging catheter or guidewire while positioned within a lumen; using the processor circuit, automatically measuring, in response to generating the plurality of image frames, an anatomical feature in the plurality of image frames; using the processor circuit and based on the automatically measured anatomical feature, identifying, from among the plurality of image frames: a target frame representing a region of interest, a proximal reference frame located proximal to the target frame, and a distal reference frame located distal to the target frame. The method further includes outputting, to a display in communication with the processor circuit, a single-screen display comprising: the proximal reference frame, the target frame, the distal reference frame, and a longitudinal representation of the lumen showing respective positions of the proximal reference frame, the target frame, and the distal reference frame. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0010] Implementations can include one or more of the following features. The method, wherein identifying the target frame includes identifying an image frame that satisfies a first criterion associated with a plaque burden or cross-sectional area of the lumen. The method, wherein identifying the proximal reference frame includes identifying an image frame that represents a closest tissue of the lumen proximal to the target frame, the image frame satisfying a second criterion associated with a plaque burden or cross-sectional area of the lumen. The method, wherein identifying the distal reference frame includes identifying an image frame that represents a closest tissue of the lumen distal to the target frame, the image frame satisfying a third criterion associated with a plaque burden or cross-sectional area of the lumen. The method further includes defining, using the processor circuit, a target region between a plurality of candidate target frames, wherein no reference frame is identified between the plurality of candidate target frames. The method, wherein identifying the target frame includes identifying an image frame that satisfies a fourth criterion associated with a plaque burden or cross-sectional area of the plurality of candidate target frames within the target region. The method, wherein identifying the target frame includes identifying an image frame that represents a desired location to be expanded with a stent. The method, wherein identifying the proximal reference frame includes identifying an image frame that represents a desired location of a proximal edge of the stent. The method, wherein identifying the distal reference frame includes identifying an image frame that represents a desired location of a distal edge of the stent. The method, wherein identifying the target frame includes identifying an image frame that has a smallest cross-sectional area within a stent positioned in the lumen. The method further includes detecting a proximal edge and a distal edge of the stent. The method, wherein identifying the proximal reference frame includes identifying an image frame that represents the proximal edge of the stent. The method, wherein identifying the distal reference frame includes identifying an image frame that represents the distal edge of the stent. Implementations of the described technology can include hardware, a method or process, or computer software on a computer-accessible medium.

[0011] Additional aspects, features, and advantages of the disclosure will become apparent from the following detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0012] Illustrative embodiments of the disclosure will be described with reference to the accompanying drawings, of which:

[0013] Figure 1 is a diagrammatic, schematic view of an intraluminal imaging system including an automated frame identification system according to aspects of the disclosure.

[0014] Figure 2 illustrates a blood vessel including a stenosis.

[0015] Figure 3 illustrates a blood vessel including a stenosis and supported open with a stent to reestablish a lumen within the blood vessel having a stenosis.

[0016] Figure 4 is a screenshot of a tomographic image containing a lumen from an example intraluminal imaging system according to at least one embodiment of the present disclosure.

[0017] Figure 5 An example visualization showing a lumen with a stent is shown according to aspects of the present disclosure.

[0018] Figure 6 A flowchart of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0019] Figure 7 A flowchart of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0020] Figure 8 A flowchart of target frame identification logic of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0021] Figure 9 A flowchart of proximal reference frame identification logic of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0022] Figure 10 A flowchart of distal reference frame identification logic of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0023] Figure 11 A flowchart of target merging logic of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0024] Figure 12 A flowchart of post-procedure logic of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0025] Figure 13 is a schematic diagram of a processor circuit according to embodiments of the present disclosure. DETAILED DESCRIPTION

[0026] An automatic frame recognition system is disclosed. Currently, there is no standardized protocol for selecting targets of interest and potential references. Attempts have been made to understand what selection criteria can be, but there is no end-to-end protocol that covers all aspects of the selection process. This presents a barrier to novice and intermediate users when analyzing images obtained during IVUS pullback. The present disclosure describes an end-to-end algorithm that takes measurements from each frame of an IVUS pullback and determines one or more targets of interest (e.g., stenosis) and corresponding proximal (healthy) and distal (healthy) reference frames to help reduce the time spent on manual pullback analysis and to help guide selection of landing zones for a stent. In the case of a post-stent placement procedure, the minimum stent area is automatically indicated. Various aspects of the algorithm can be configurable to match individual preferences and experiences to help a greater audience. The algorithm can also be referred to as a system, process, procedure, method, decision tree, or decision matrix.

[0027] The algorithm provides an automatic, standardized, quantitative method for finding one or more target of interest frames and reference frames for each target from a given pullback starting with per-frame metrics. The process represents the secondary level steps to follow after a complete pullback image dataset has been acquired and all required per-frame metrics have been computed for each frame in the dataset, and thus can sometimes be referred to as the “secondary logic.” This logic automatically identifies targets of interest in pre-procedural data (e.g., based on minimum lumen diameter or other considerations), “minimum stent area” (MSA) frames in post-procedural data (e.g., based on minimum lumen diameter within the stent), and landing zones (e.g., for placement starting and ending points) (e.g., based on the closest tissue within the vessel that meets certain criteria deemed healthy).

[0028] In addition to automatic detection of target and reference frames, the algorithm includes user settings that change the detection criteria. The algorithm can be applied to automatic pullback analysis and workflows, or any scenario where per-frame measurements are available for an entire pullback or captured image sequence or portion thereof. Information derived by the algorithm can be used, for example, to determine a desired length and diameter of a stent, a desired location to place a stent within a vessel, and a desired degree of expansion of a stent post-placement. The algorithm advises a clinician or other physician, and can be used as a starting point for clinical decision making.

[0029] The devices, systems, and methods described herein can include one or more features described in U.S. Provisional Application US 62 / 643105 (Attorney Docket No. 2017PF 02102), filed March 14, 2018, U.S. Provisional Application US 62 / 642847 (Attorney Docket No. 2017PF 02103), filed March 14, 2018, U.S. Provisional Application US 62 / 712009 (Attorney Docket No. 2017PF 02296), filed July 30, 2018, and U.S. Provisional Application US 62 / 643366 (Attorney Docket No. 2017PF 02365), filed March 15, 2018, and "Intravascular Ultrasound Versus Angiography-Guided Drug-Eluting Stent Implantation: The ULTIMATE Trial" (Junjie Zhang et al., JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, Volume 72, Number 24, Pages 3126-3137 (2018)), each of which is incorporated herein by reference in its entirety as if fully set forth herein.

[0030] By improving the quantitative analysis and comparison of all frames in a data set, the present disclosure substantially facilitates the reproducible identification of optimal target and reference frames in intraluminal image data sets. The automatic frame identification system disclosed herein, implemented on an intraluminal imaging system in communication with an intraluminal imaging probe, provides practical quantitative guidance to a clinician in selecting dimensions and landing zones for stents and other treatments. This streamlined and enhanced workflow converts a tedious, error-prone manual process into a numerically rigorous automated selection without the normal routine need for manual performance of calculations or comparisons, flipping through candidate images one by one, and "eyeballing" diseased and nearby healthy tissue to identify target and reference regions of interest. This unconventional approach improves the functioning of intraluminal imaging systems by improving the speed and consistency of outcomes associated with positive outcomes.

[0031] The automatic frame identification system can be implemented as a logic tree that produces outputs observable on a display and operated by a control process executing on a processor that accepts user input from a keyboard, mouse, or touch screen interface and communicates with one or more intraluminal sensing devices. In this regard, the control process performs certain specific operations in response to different inputs, selections, or value edits made at different points in execution. Certain structures, functions, and operations of the processor, display, sensors, and user input system are known in the art, while others are recited herein to specifically implement novel features or aspects of the present disclosure.

[0032] These descriptions are provided only for purposes of example and should not be considered limiting the scope of the automated frame recognition system. Certain features can be added, removed, or modified without departing from the spirit of the claimed subject matter.

[0033] For the purpose of promoting an understanding of the principles of the present disclosure, reference will now be made to the embodiments illustrated in the drawings, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is intended. Alterations and further modifications of the described devices, systems, and methods, and any further applications of the principles of the disclosure are fully contemplated and expected to be within the scope of the disclosure. In particular, it is fully contemplated that features, components, and / or steps described with respect to one embodiment can be combined with features, components, and / or steps described with respect to other embodiments of the disclosure. However, for the sake of brevity, many iterations of these combinations will not be described again in detail.

[0034] Figure 1 is a diagrammatical, schematic illustration of a luminal imaging system including an automated frame recognition system in accordance with aspects of the present disclosure. In some embodiments, the luminal imaging system 100 can be an intravascular ultrasound (IVUS) imaging system. The luminal imaging system 100 can include a luminal device 102, a patient interface module (PIM) 104, a console or processing system 106, a monitor 108, and an external imaging system 132, which can include angiography, ultrasound, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), or other imaging technologies, equipment, and methods. The luminal device 102 can be sized and shaped and / or otherwise structurally arranged to be positioned within a body lumen of a patient. For example, in various embodiments, the luminal device 102 can be a catheter, a guidewire, a guide catheter, a pressure guidewire, and / or a flow guidewire. In some cases, the system 100 can include additional elements and / or can be implemented without Figure 1 One or more of the illustrated elements can be omitted in implementations of the system 100. For example, the system 100 can omit the external imaging system 132.

[0035] The luminal imaging system 100 (or intravascular imaging system) can be any type of imaging system suitable for use in a patient's lumen or vasculature. In some embodiments, the luminal imaging system 100 is an intravascular ultrasound (IVUS) imaging system. In other embodiments, the luminal imaging system 100 can include systems configured for the following imaging modalities: forward-looking intravascular ultrasound (FL-IVUS) imaging, intravascular photoacoustic (IVPA) imaging, intracardiac echocardiography (ICE), transesophageal echocardiography (TEE), and / or other suitable imaging modalities.

[0036] It should be appreciated that the system 100 and / or the device 102 can be configured to obtain any suitable intraluminal imaging data. In some embodiments, the device 102 can include imaging components of any suitable imaging modality (e.g., optical coherence tomography (OCT), etc.). In some embodiments, the device 102 can include any suitable non-imaging components, including pressure sensors, flow sensors, temperature sensors, optical fibers, reflectors, mirrors, prisms, ablation elements, radiofrequency (RF) electrodes, conductors, or combinations thereof. Generally, the device 102 can include imaging elements to obtain intraluminal imaging data associated with the lumen 120. The device 102 can be sized and shaped (and / or configured) for insertion into a blood vessel or lumen 120 of a patient.

[0037] The system 100 can be deployed in a catheterization laboratory having a control room. The processing system 106 can be positioned in the control room. Optionally, the processing system 106 can be positioned elsewhere, such as in the catheterization laboratory itself. The catheterization laboratory can include a sterile field, while its associated control room can or can not be sterile depending on the procedure to be performed and / or the healthcare facility. The catheterization laboratory and control room can be used to perform any number of medical imaging procedures, such as angiography, fluoroscopy, CT, IVUS, virtual histology (VH), forward looking IVUS (FL-IVUS), intraluminal photoacoustic (IVPA) imaging, fractional flow reserve (FFR) determination, coronary flow reserve (CFR) determination, optical coherence tomography (OCT), computed tomography, intracardiac echocardiography (ICE), forward looking ICE (FLICE), intraluminal palpography, transesophageal ultrasound, fluoroscopy, and other medical imaging modalities or combinations thereof. In some embodiments, the device 102 can be controlled from a remote location (e.g., the control room) such that an operator does not need to be in close proximity to the patient.

[0038] The intraluminal imaging device 102, the PIM 104, the monitor 108, and the external imaging system 132 can be communicatively coupled, directly or indirectly, to the processing system 106. These elements can be communicatively coupled to the medical processing system 106 via wired connections (e.g., standard copper links or fiber optic links) and / or via wireless connections (using the IEEE 802.11 Wi-Fi standard, the Ultra-Wide Band (UWB) standard, Bluetooth®, wireless Firewire, wireless USB, or another high-speed wireless networking standard). The processing system 106 can be communicatively coupled to one or more data networks, such as a TCP / IP-based local area network (LAN). In other embodiments, different protocols can be utilized, such as Synchronous Optical Networking (SONET). In some cases, the processing system 106 can be communicatively coupled to a wide area network (WAN). The processing system 106 can utilize network connections to access various resources. For example, the processing system 106 can communicate with a Digital Imaging and Communications in Medicine (DICOM) system, a Picture Archiving and Communication System (PACS), and / or a hospital information system via network connections.

[0039] The intraluminal ultrasound imaging device 102 emits ultrasound energy at a high level from a transducer array 124 included in a scanner assembly 110 mounted near the distal end of the intraluminal device 102. The ultrasound energy is reflected by tissue structures in the medium (e.g., the lumen 120) surrounding the scanner assembly 110, and the transducer array 124 receives ultrasound echo signals. The scanner assembly 110 generates electrical signal(s) representative of the ultrasound echoes. The scanner assembly 110 can include one or more single ultrasound sensors and / or transducer arrays 124 in any suitable configuration (e.g., a planar array, a curved array, a circumferential array, a ring array, etc.). For example, in some instances, the scanner assembly 110 can be a one-dimensional array or a two-dimensional array. In some instances, the scanner assembly 110 can be a rotating ultrasound device. The active zone of the scanner assembly 110 can include one or more segments (e.g., one or more rows, one or more columns, and / or one or more orientations) of one or more transducer materials and / or ultrasound elements that can be controlled and activated in unison or independently. The active zone of the scanner assembly 110 can be patterned or structured in various basic or complex geometries. The scanner assembly 110 can be disposed in a side-looking orientation (e.g., ultrasound energy emitted perpendicular to and / or orthogonal to the longitudinal axis of the intraluminal device 102) and / or a forward-looking orientation (e.g., ultrasound energy emitted parallel to and / or along the longitudinal axis). In some instances, the scanner assembly 110 is structurally arranged to emit and / or receive ultrasound energy in a proximal direction or a distal direction at an oblique angle relative to the longitudinal axis. In some embodiments, the ultrasound energy emission can be electronically steered by selectively firing one or more transducer elements of the scanner assembly 110.

[0040] The ultrasound sensor(s) of the scanner assembly 110 can be piezoelectric micromachined ultrasonic transducers (PMUTs), capacitive micromachined ultrasonic transducers (CMUTs), single crystals, lead zirconate titanate (PZT), PZT composites, other suitable transducer types, and / or combinations thereof. In embodiments, the ultrasound transducer array 124 can include any suitable number of individual transducer elements or acoustic elements, between 1 acoustic element and 1000 acoustic elements, e.g., 2 acoustic elements, 4 acoustic elements, 36 acoustic elements, 64 acoustic elements, 128 acoustic elements, 500 acoustic elements, 812 acoustic elements, and / or more and fewer acoustic elements.

[0041] The PIM 104 transmits the received echo signals to the processing system 106, where the ultrasound images, including flow information, are reconstructed and displayed on the monitor 108. The console or processing system 106 can include a processor and a memory. The processing system 106 can be operable to facilitate the features of the intraluminal imaging system 100 described herein. For example, the processor can execute computer-readable instructions stored on a transitory tangible computer-readable medium.

[0042] The PIM 104 facilitates signal communication between the processing system 106 and the scanner assembly included in the intraluminal device 102. Such communication can include providing commands to the integrated circuit controller chip(s) within the intraluminal device 102, selecting particular element(s) on the transducer array 124 for transmission and reception, providing a transmit trigger signal to the integrated circuit controller chip(s) to activate the transmitter circuit to generate an electrical pulse to excite the selected transducer array element(s), and / or receiving echo signals received from the selected transducer array element(s) amplified via an amplifier included on the integrated circuit controller chip(s). In some embodiments, the PIM 104 performs preliminary processing on the echo data before relaying the data to the processing system 106. In examples of such embodiments, the PIM 104 performs amplification, filtering, and / or aggregation of the data. In embodiments, the PIM 104 also supplies high and low voltage DC power to support operation of the intraluminal device 102 including the circuitry within the scanner assembly 110.

[0043] Processing system 106 receives echo data from scanner assembly 110 via PIM 104 and processes the data to reconstruct an image of tissue structures in the medium surrounding scanner assembly 110. Typically, device 102 can be used within any suitable anatomical structure and / or body lumen of the patient. Processing system 106 outputs image data such that an image of a blood vessel or lumen 120, e.g., a cross-sectional IVUS image of lumen 120, is displayed on monitor 108. Lumen 120 can represent naturally fluid-filled or fluid-enclosed structures and artificial fluid-filled or fluid-enclosed structures. Lumen 120 can be within the patient's body. Lumen 120 can be a blood vessel, e.g., an artery or vein of the patient's vascular system (including the cardiac vascular system, peripheral vascular system, neurovascular system, renal vascular system, and / or any other suitable lumen within the body). For example, device 102 can be used to examine any number of anatomical locations and tissue types, including but not limited to organs (including the liver, heart, kidneys, gallbladder, pancreas, and lungs), ducts, intestines, nervous system structures (including the brain, dura mater, spinal cord, and peripheral nerves); the urinary tract; and valves within the heart's blood vessels, chambers, or other parts and / or other systems of the body. In addition to natural structures, device 102 can also be used to examine artificial structures, such as, but not limited to, heart valves, stents, shunts, filters, and other devices.

[0044] The controller or processing system 106 may include processing circuitry having one or more processors that communicate with memory and / or other suitable tangible computer-readable storage media. The controller or processing system 106 may be configured to perform one or more aspects of this disclosure. In some embodiments, the processing system 106 and the monitor 108 are separate components. In other embodiments, the processing system 106 and the monitor 108 are integrated into a single component. For example, system 100 may include a touchscreen device, including a housing with a touchscreen display and a processor. System 100 may include any suitable input device (e.g., a touchpad or touchscreen display, keyboard / mouse, joystick, buttons, etc.) for a user to select options displayed on the monitor 108. The processing system 106, monitor 108, input devices, and / or combinations thereof may be referred to as the controller of system 100. This controller is capable of communicating with device 102, PIM 104, processing system 106, monitor 108, input devices, and / or other components of system 100.

[0045] In some embodiments, the intraluminal device 102 includes some components similar to conventional solid-state IVUS catheters (e.g., EagleEye available from Philips). The intraluminal device 102 can include a scanner assembly 110 near a distal end of the intraluminal device 102 and a transmission line bundle 112 extending along a longitudinal body of the intraluminal device 102. The cable or transmission line bundle 112 can include a plurality of conductors, including one, two, three, four, five, six, seven, or more conductors.

[0046] The transmission line bundle 112 terminates in a PIM connector 114 at a proximal end of the intraluminal device 102. The PIM connector 114 electrically couples the transmission line bundle 112 to the PIM 104 and physically couples the intraluminal device 102 to the PIM 104. In embodiments, the intraluminal device 102 also includes a guidewire exit 116. Thus, in some instances, the intraluminal device 102 is a rapid exchange catheter. The guidewire exit 116 allows for insertion of a guidewire 118 distally to guide the intraluminal device 102 through the lumen 120.

[0047] The monitor 108 can be a display device, such as a computer monitor or other type of screen. The monitor 108 can be used to display selectable prompts, instructions, and visualizations of the imaging data to a user. In some embodiments, the monitor 108 can be used to provide a procedure-specific workflow to a user to complete an intraluminal imaging procedure. The workflow can include performing a pre-stent placement plan to determine a lumen status and stent potential, and the workflow can also include a post-stent placement check to determine a status of a stent that has been positioned in the lumen. The workflow can be presented to the user in any of the displays or visualizations shown or other displays. Figures 4-6

[0048] The external imaging system 132 can be configured to obtain X-ray images, radiographic images, angiographic images (e.g., with contrast), and / or fluoroscopic images (e.g., without contrast) of the patient’s body, including the blood vessel 120. The external imaging system 132 can also be configured to obtain computed tomography images of the patient’s body, including the blood vessel 120. The external imaging system 132 can include an external ultrasound probe configured to obtain ultrasound images of the patient’s body, including the blood vessel 120, when positioned outside the body. In some embodiments, the system 100 includes other imaging modality systems (e.g., MRI) to obtain images of the patient’s body, including the blood vessel 120. The processing system 106 can utilize the images of the patient’s body in conjunction with the intraluminal images obtained by the intraluminal device 102.

[0049] Figure 2 ​A blood vessel 200 is illustrated that includes a stenosis 230. The stenosis occurs between the blood vessel walls 210 and can limit the flow of blood 220. Stenosis has many types, including atherosclerosis.

[0050] Figure 3 A blood vessel 200 is illustrated that includes a stenosis 230 and is supported open with a stent 340 to reestablish the lumen within the blood vessel with the stenosis. The stent 340 compresses and blocks the stenosis 330, opening the blood vessel 300 and preventing the stenosis 230 from progressing through the blood vessel 200. The stent 340 also pushes the blood vessel walls 310 outward, thereby reducing the restriction on the flow of blood 220. Other treatment options for mitigating the obstruction can include, but are not limited to, thrombectomy, ablation, angioplasty, and administration of medication. However, in most cases, it can be highly desirable to obtain an intravascular image of the affected region accurately and in time, as well as accurate and detailed knowledge of the location of the affected region before, during, or after treatment. Inaccurate or imprecise location or orientation information of the IVUS image can carry the risk of, for example, ablation or stent implantation of healthy tissue instead of diseased tissue during treatment.

[0051] Figure 4 A screenshot 400 of an example intraluminal imaging system 100 of tomographic images 410 containing a lumen 120 according to at least one embodiment of the present disclosure. Such tomographic images are radial or axial cross-sectional images perpendicular to the longitudinal axis of the blood vessel, and can generate a tomographic image frame, and can automatically make its measurements while the pullback occurs. The screenshot 600 also includes an angiogram image or graphical roadmap image 430, and an image longitudinal display (ILD) 420 composed of a plurality of stacked cross-sectional tomographic images from a pullback sequence or graphical representations thereof. The ILD 420 is a longitudinal cross-sectional view parallel to or including the longitudinal axis of the blood vessel. In some embodiments, the ILD 420 can include images from a pullback that are captured along the length of the blood vessel and shown in a longitudinal representation along the length of the blood vessel. In other embodiments, the ILD 420 can be a graphical representation of the geometric properties (e.g., diameter or cross-sectional area) of the blood vessel at different locations. Other information can be displayed instead of or in addition to that shown here.

[0052] Within the angiogram image or graphical roadmap image 430 are a target location marker 440, a proximal reference location marker 450, a distal reference location marker 460, and several blood vessel side branches 470. In some instances, it can not be desirable for the target marker 440 or reference markers 450 or 460 to be co-located with the side branches 470, and thus in some embodiments, the algorithm includes logic to avoid such a situation. The ILD 420 also includes the target marker 440, the proximal reference marker 450, and the distal reference marker 460, each indicating a particular location or frame within the pullback sequence.

[0053] Figure 5 An exemplary visualization 500 showing the lumen with stent 340 is shown in accordance with aspects of the present disclosure. The visualization 500 includes a longitudinal representation or ILD 420, a distal reference frame 504, a target frame 508, and a proximal reference frame 506. The frames 504, 508, and 506 are tomographic images selected by an algorithm from a plurality of images in an IVUS pullback sequence. The ILD 420 includes a target marker 440, a proximal reference marker 450, and a distal reference marker 460. In a pre-procedural context, the target marker 440 and the target frame 508 can represent an automatically detected location of the minimum lumen diameter or area, while the proximal reference marker, the distal reference marker, the proximal reference frame, and the distal reference frame can represent an automatically detected starting location of healthy tissue, and thus a desired location of the proximal and distal edges of the stent. In a post-procedural context, the target marker 440 and the target frame 508 can represent an automatically detected location of the MSA or the minimum cross-sectional area of the stent 340, while the proximal reference marker 450, the proximal reference frame 506, the distal reference marker 460, and the distal reference frame 504 can represent an automatically detected proximal and distal edges of the stent 340. In some instances, the region of the lumen 120 covered by the stent 340 is referred to as the landing zone or LZ.

[0054] The ILD 420, the target marker 440, the proximal reference marker 450, the distal reference marker 460, the distal reference frame 504, the target frame 508, and the proximal reference frame 506 together displayed on a single screen (or single user interface element) can collectively provide guidance to the clinician. In a pre-procedural context, this information can suggest to the clinician the desired size (diameter and length) of the stent 340 and the landing zone needed to treat the stenosis 230. In a post-procedural context, this information can suggest to the clinician whether the stent 340 is properly positioned, whether the stent 340 needs to be expanded, and if so, where and by how much.

[0055] Imaging data from which the frames 504, 506, and 508 are automatically detected can be collected by the device 102 as the device 102 moves through the lumen 120 or through the stent 340 within the lumen 120. In some embodiments, the system can automatically detect the location of the stent 340 and display a visualization of the stent 340 on the visualization 500. The frames 504, 506, 508 can be visually related to the longitudinal view via the corresponding markers 460, 450, and 440. In other embodiments, the frames 504, 506, 508 can be shown laterally with color, symbols, shapes, text boxes, or other visual indicators.

[0056] The diameter and area of the stent 340 in each of the lateral views 504, 506, 508 can be automatically calculated and compared to other imaging data. For example, the calculated area and diameter of each of the lateral views 504, 506, 508 can be compared to corresponding measurements in the pre-stent procedure. In this way, the operator can be able to check the effectiveness of the placed stent 340. Misalignment or poor apposition of the stent 340 can also be automatically detected and displayed by the system. These aspects can be accompanied by visual cues, such as highlighted regions or symbols, and can have associated warnings to alert the operator of their presence.

[0057] In some embodiments, the ILD 420 includes a pressure graph 510 that shows measured pressure values along each location of the lumen 120 or vessel 200.

[0058] Figure 6 A flowchart 600 of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown. The flowchart 600 includes a pre-procedure assessment step 610 (including steps 630-680) and a post-procedure assessment and optimization step 620 (including steps 690-696).

[0059] In step 630, the plaque burden and lumen area of the lumen 120 or vessel 200 are assessed for each frame of the image sequence (e.g., IVUS pullback sequence).

[0060] In step 640, the collected image frames are analyzed to determine potential or desired landing zones for one or more stents 340.

[0061] In step 650, pressure readings along the length of the lumen 120 or vessel 200 are correlated to the location of the tomographic images taken during the pullback and can be displayed, for example, as a longitudinal pressure graph 510 on the ILD 420.

[0062] In step 660, the external elastic membrane (EEM) and cross-sectional area and diameter of the lumen region of the vessel are calculated for each frame of the pullback. Other anatomical features and measurements can also be calculated depending on the implementation.

[0063] In step 670, the clinician uses the visualizations, calculations, and frame selections provided by the automatic frame identification system to plan the step-by-step details of the procedure to implant one or more stents.

[0064] In step 680, the clinician implants one or more stents in one or more of the identified desired landing zones in the vessel or lumen, or dilates the vessel in one or more of the identified desired landing zones using a balloon.

[0065] In step 690, the lumen 120 or vessel 200 is evaluated proximally and distally of the stent(s) to ensure that the tissue all the way to the edges of the stent(s) is healthy. If the stent is malpositioned or not dilated sufficiently, a“dog bone” or widening of the ends of the stent relative to the center can occur.

[0066] In step 692, the stent itself is evaluated to determine whether the minimum stent area (MSA) is within an expected or desired range. In many cases, the location of the MSA is expected to coincide with the location of the distal reference frame, as the distal portion of a healthy vessel or lumen can naturally be narrower than the proximal portion of the same vessel or lumen.

[0067] In step 694, if stent dilation is indicated, the clinician inserts a non-compliant balloon into the stent and expands the balloon by the measured amount, thereby dilating the stent to the desired diameter indicated by the automated frame identification system.

[0068] In step 696, the stent and lumen are reevaluated, and if any issues are detected, execution returns to step 690. Otherwise, the flow ends.

[0069] Figure 7 A flowchart 700 of an example automated frame identification method according to at least one embodiment of the present disclosure is shown.

[0070] In step 710, an image data set is captured, for example during an IVUS pullback procedure that captures a sequence of tomographic images from within the lumen 120 or vessel 200 as the imaging probe 102 is pulled through the lumen 120 or vessel 200.

[0071] In step 720, the boundaries of the lumen 120 or vessel 200 are automatically identified in each frame of the sequence using image recognition.

[0072] In step 730, frames showing the inside of the catheter sheath are automatically detected and removed from the image sequence so that they do not affect the operation of the algorithm. The sheath is a guide catheter inserted into the blood vessel prior to the imaging catheter / wire. The imaging catheter / wire is guided through the sheath lumen to the location in the blood vessel. For example, the imaging element (ultrasound transducer, OCT element) is positioned distal to the lesion while the distal end of the sheath is positioned proximal to the lesion. The imaging catheter / wire obtains imaging data during pullback so that the imaging element moves longitudinally proximally towards the sheath. This is why the proximal side of the pullback sequence can include tomographic images representing the sheath (e.g., captured inside the sheath). In pullback, the image frames with the sheath are at the end. If the user moves the imaging catheter / guide from the proximal direction to the distal direction (opposite to the pullback direction), the image frames with the sheath will be at the beginning. In some instances, the imaging probe extends from the catheter sheath to a point inside the lumen or vessel and then is pulled back through the lumen or vessel until it re-enters the catheter sheath. Thus, the sheath frames can be found near the beginning or end of the image sequence. Note that in some embodiments, step 730 can occur before step 720.

[0073] In step 740, per-frame metrics are computed for each non-removed frame in the sequence. The per-frame metrics can include plaque burden (PB) and lumen area (LA) as well as other anatomic measurements.

[0074] In step 750, a target frame is automatically identified based on the per-frame metrics, as described below. The target frame represents an anatomic region of interest, such as a stenosis. In examples, this region of interest is a potential or recommended treatment location, such as a location corresponding to an image frame with a relatively smaller diameter, area measurement, or relatively larger plaque burden, etc. If a target is found, execution moves to step 770. If not, execution moves to step 760.

[0075] In step 760, the method reports that it was unable to identify a target frame based on specified criteria as described below. This may, for example, encourage the clinician to adjust the search criteria so that a target frame can be identified. Alternatively, it can mean that the disease state of the blood vessel or lumen is not sufficient to warrant treatment.

[0076] In step 770, the algorithm reports the identified target frame, including the relevant per-frame metrics as described above.

[0077] In step 780, proximal and distal reference frames representing the closest healthy tissue to the target frame are automatically identified, as described below. If one or both references are not found, execution moves to step 790. Otherwise, execution moves to step 799.

[0078] In step 790, the method reports that it was unable to identify the required reference frames based on the specified criteria as described below. This can for example encourage the clinician to adjust the search criteria so that the proximal and distal frames can be identified.

[0079] In step 799, the method reports (e.g., displays) the identified proximal and distal reference frames, and the associated per-frame metrics.

[0080] Figure 8 A flowchart 800 of the target frame identification logic of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0081] In step 810, the system captures the image data set as described above.

[0082] In step 815, the system examines each frame of the data set until it finds a frame with a plaque burden (PB) greater than a first threshold THRESH1 and a lumen area (LA) less than a second threshold THRESH2. In an example, THRESH1 and THRESH2 are user-editable parameters, but the system can also define default values for each parameter (e.g., for an exemplary coronary arterial vasculature, THRESH1 = 70% and THRESH2 = 4 mm 2 but other values can be used depending on the anatomy under consideration). If no such candidate frame can be identified in the data set, execution proceeds to step 820. If such a candidate frame is identified, execution proceeds to step 835.

[0083] In step 820, the algorithm fails to identify a target frame that meets the specified criteria, so the algorithm instead finds a sequence of THRESH4a frames in the image data set with the largest plaque burden (with a tolerance of ±THRESH4b) and reports this information to the clinician. This information can be used, for example, to determine whether to revise the target frame detection criteria, or whether to postpone the procedure. In an example, THRESH4a and THRESH4b are user-editable parameters, but the system can also specify default values.

[0084] In step 830, the algorithm reports that no target frame has yet been found.

[0085] In step 835, once a potential target frame has been identified in step 815, the algorithm examines the next THRESH3 frames to see if they also meet the criteria for a target frame. If not, execution returns to step 815 to continue scanning the frames. If so, execution proceeds to step 840. In an example, THRESH3 is a user-editable parameter, but the system can also specify a default value.

[0086] In step 840, the identified frame is recorded as the target frame, and the process proceeds to step 850.

[0087] In step 850, the system reports the identified target frames and their associated per-frame metrics (e.g., plaque load and lumen area).

[0088] In step 860, the system performs the following... Figure 9 The near-side reference frame identification logic described in the document.

[0089] In step 870, the system performs the following... Figure 10 The far-side reference frame identification logic is described in [the document]. It should be noted that in some embodiments, step 870 may occur before step 860. It should also be noted that per-frame metrics of either the near-side or far-side reference frame can be compared with the target frame to calculate values ​​such as percentage narrowing.

[0090] In step 880, the method determines which target frame (far or near) will be used in cases where no reference is found between the two targets (e.g., when the two targets are to be merged). If the far target frame shows at least one of a smaller lumen area or a larger plaque load than the near target frame, then in step 890, the far frame is recorded as the absolute target frame. Otherwise, in step 895, the near frame is recorded as the absolute target frame.

[0091] It should be noted that the method can also be configured such that the terms "distal" and "proximal" are exchanged at least in steps 880, 890 and 895.

[0092] Figure 9 A flowchart 900 is shown of the proximal reference frame identification logic of an example automatic frame identification method according to at least one embodiment of the present disclosure.

[0093] In step 910, the method initiates proximal reference frame identification logic for each target identified in the dataset.

[0094] In step 920, the method identifies the first frame that is proximal to the target and has a plaque load less than THRESH5. In the example, THRESH5 is a user-editable parameter, but the system can also define a default value (e.g., 40%).

[0095] In step 930, the method determines whether a frame satisfying the THRESH5 criterion can be found before encountering a frame satisfying the THRESH5 criterion. Figure 8 Another frame of the target criteria. If so, proceed to step 940, where the potential target frame is merged with the existing target, as follows: Figure 11 As described in [the document], the process is executed and then returns to step 920. If no intermediate target frame is found, execution proceeds to step 950.

[0096] In step 950, the method checks to see if the frame-by-frame search has reached the THRESH0 frames from the start of the sheath. If so, execution proceeds to step 990 and the method reports that no proximal reference was found. If not, the identified candidate proximal reference is mentioned to step 960. In examples, THRESH0 is a user-editable parameter, although the system can also specify a default value.

[0097] In step 960, the method checks to see if the identified candidate proximal reference frame is on the lumen 120 or a side branch 470 of the vessel 200 (e.g., as shown in Figure 4 If so, execution proceeds to step 970. If not, execution proceeds to step 965, where the identified candidate proximal reference is flagged as a proximal reference frame.

[0098] In step 970, the method searches backward (e.g., distally) to find the closest frame that does not include the side branch 470, which then becomes the candidate proximal reference frame.

[0099] In step 980, the method determines whether the candidate proximal reference frame shows a plaque burden less than THRESH6. If not, execution returns to step 920. If so, execution proceeds to step 965, where the identified candidate proximal reference is flagged as a proximal reference frame. In examples, THRESH6 is a user-editable parameter, although the method can also specify a default value. In other embodiments, depending on the implementation, the proximal reference frame can represent the closest healthy tissue proximal to the target frame, where “healthy” is defined as completely healthy tissue, or as tissue whose disease burden is less than that of the target frame and that meets criteria associated with at least one of plaque burden or lumen cross-sectional area.

[0100] Figure 10 A flowchart 1000 showing distal reference frame identification logic of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0101] In step 1010, the method initiates distal reference frame identification logic for each target identified in the dataset.

[0102] In step 1020, the method identifies the first frame distal to the target and having a plaque burden less than THRESH5. In examples, THRESH5 is a user-editable parameter, although the method can also define a default value.

[0103] In step 1030, the method determines whether a frame can be found that meets the criteria of THRESH5 before a frame that meets the criteria of THRESH6 is encountered. If so, execution proceeds to step 1040. If not, execution proceeds to step 1050, where the frame that meets the criteria of THRESH6 is flagged as a distal reference frame. Figure 8another frame of the ground truth criteria. If yes, execution moves to step 1040, where the potential target frame is merged with the existing target as described below in Figure 11 step 1020. If no intermediate target frame is found, execution proceeds to step 1050.

[0104] In step 1050, the method checks to see if the frame-by-frame search has reached the end of the image sequence (e.g., the first or last frame in the sequence). If yes, execution proceeds to step 1090 and the method reports that no distal reference was found. If no, the identified candidate distal reference is referred to step 1060.

[0105] In step 1060, the method checks to see if the identified candidate distal reference frame is on the lumen 120 or a side branch 470 of the vessel 200 (e.g., as shown in Figure 4 FIG. 6B). If yes, execution proceeds to step 1070. If no, execution proceeds to step 1065, where the identified candidate distal reference frame is flagged as a distal reference frame.

[0106] In step 1070, the method searches backward (e.g., proximally) to find the closest frame that does not include the side branch 470, which then becomes the candidate distal reference frame.

[0107] In step 1080, the method determines whether the candidate distal reference frame shows a plaque burden that is less than THRESH6. If no, execution returns to step 1020. If yes, execution proceeds to step 1065, where the identified candidate distal reference frame is flagged as a distal reference frame. In examples, THRESH6 is a user-editable parameter, although the method can also specify a default value. In other embodiments, depending on the implementation, the distal reference frame can represent the closest healthy tissue distal to the target frame, where “healthy” is defined as completely healthy tissue, or as tissue whose disease burden is less than that of the target frame and satisfies a criterion associated with at least one of plaque burden or lumen cross-sectional area.

[0108] Figure 11 A flowchart 1100 showing target merging logic for an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0109] In step 1110, the method initiates the steps for each target identified in the dataset.

[0110] In step 1120, the proximal and distal reference frames are identified as shown above in Figure 9 and 10 .

[0111] In step 1130, the method determines whether the target frame needs to be merged (as shown above, for example in steps 930 and 1030). If no, execution proceeds to step 1140 (END), and the target merge logic takes no action on the current target. If yes, execution proceeds to step 1150.

[0112] In step 1150, the consecutive targets between which there is no proximal or distal reference are "merged", or filtered, to select one target frame over the other using the merge criteria. Two targets are filtered at a time; in each comparison, there is a more proximal target and a more distal target. If the distal target frame has at least one of a smaller lumen area or a greater plaque burden than the proximal target frame, the distal target frame is selected as the absolute target frame for the region to be displayed, and the per-frame value of the distal target is used as the target per-frame value; otherwise, the proximal target frame is selected as the absolute target frame for the region to be displayed, and the per-frame value of the proximal frame is used as the target per-frame value. This "merging" or elimination continues until all candidate target frames between which there is no reference frame are filtered. The target merge logic is then complete. In some embodiments, the target is selected based on both the maximum plaque burden and the minimum lumen area. In some embodiments, the maximum plaque burden is weighted more heavily than the minimum lumen area. In other embodiments, the minimum lumen area is weighted more heavily than the maximum plaque burden. In other embodiments, the selected lumen satisfies another criterion associated with at least one of plaque burden or lumen cross-sectional area.

[0113] It is noted that the method can also be configured such that the terms "distal" and "proximal" are swapped in at least step 1150.

[0114] Figure 12 A flowchart 1200 of post-procedure logic of an example automatic frame identification method according to at least one embodiment of the present disclosure is shown.

[0115] In step 1210, a post-procedure image dataset is captured as described above.

[0116] In step 1220, per-frame metrics are calculated as described above.

[0117] In step 1230, stents, if any, are identified using image recognition, and the image frames containing the proximal and distal stent edges are marked as proximal and distal reference frames.

[0118] In step 1240, the proximal and distal reference frames containing the stent edges are displayed (for example as shown in FIG. 12B). Figure 5

[0119] In step 1250, the minimum stent area (MSA) frame is identified based on the per-frame metrics, and marked as the target frame.​

[0120] In step 1260, the target frame is displayed (e.g., as shown in FIG. 12B). Figure 5

[0121] It is noted that the MSA can be detected and displayed within the identified stent region, and can also be detected and displayed from one or more sub-regions within the stent. The one or more sub-regions can be automatically identified, or can be defined by user input based on criteria or thresholds that can be edited by the user. For example, the sub-regions can be a proximal region, a central region, and / or a distal region of the stent. In some embodiments, the sub-regions can be identified based on the length of the stent using, for example, intraluminal imaging data, extraluminal imaging data, and / or a length of the stent provided to the processor circuit. Detecting and displaying the MSA within the sub-region(s) within the stent can be used to verify that different portions of the stent have been properly expanded within the vessel.

[0122] One or more of the methods 600, 700, 800, 900, 1000, 1100, and / or 1200 can include a step of comparing the measured and / or anatomical quantity to a threshold value. In some embodiments, such a step or other steps can include determining a value (e.g., a numerical value) of the measurement and a value (e.g., a numerical value) of the threshold value. The comparison can be determining when the value of the measurement reaches the value of the threshold value (e.g., when the measurement equals the threshold value, when the measurement exceeds the threshold value, and / or when the measurement is less than the threshold value).

[0123] Figure 13 is a schematic diagram of a processor circuit 1350 according to embodiments of the present disclosure. The processor circuit receives imaging data from an intraluminal device. The processor circuit 1350 can be implemented in an ultrasound imaging system 100 or other device or workstation (e.g., a third-party workstation, a network router, etc.) necessary to implement the method. As shown, the processor circuit 1350 can include a processor 1360, a memory 1364, and a communication module 968. These elements can be in direct or indirect communication with one another (e.g., via one or more buses).

[0124] ​The processor 1360 can include a central processing unit (CPU), a digital signal processor (DSP), an ASIC, a controller, or any combination of general-purpose computing devices, reduced instruction set computing (RISC) devices, application- specific integrated circuits (ASICs), field programmable gate array (FPGAs), or other related logic devices (including mechanical and quantum computing devices). The processor 1360 can also include another hardware device configured to perform the operations described herein, a firmware device, or any combination thereof. The processor 1360 can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0125] The memory 1364 can include cache memory (e.g., of the processor 1360), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, solid state memory device, hard disk drive, other forms of volatile and nonvolatile memory, or a combination of different types of memory. In an embodiment, the memory 1364 includes a non-transitory computer-readable medium. The memory 1364 can store instructions 1366. The instructions 1366 can include instructions that, when executed by the processor 1360, enable the processor 1360 to perform operations described herein. The instructions 1366 can also be referred to as code. The terms “instructions” and “code” should be interpreted broadly to include any type of computer-readable statement(s). For example, the terms “instructions” and “code” can refer to one or more programs, routines, sub-routines, functions, flows, and the like. “Instructions” and “code” can include a single computer-readable statement or many computer-readable statements.

[0126] The communication module 1368 can include any electronic and / or logic circuitry to facilitate direct or indirect communication of data between the processor circuit 1350 and other processors or devices. In this regard, the communication module 1368 can be an input / output (I / O) device. In some instances, the communication module 1368 facilitates direct or indirect communication of the processor circuit 1350 and / or various elements of the ultrasound imaging system 100. The communication module 1368 can communicate in the processor circuit 1350 by a variety of methods or protocols. Serial communication protocols can include, but are not limited to, US SPI, I2C, RS-232, RS-485, CAN, Ethernet, ARINC 429, MODBUS, MIL-STD-1553, or any other suitable method or protocol. Parallel protocols include, but are not limited to, ISA, ATA, SCSI, PCI, IEEE-488, IEEE-1284, and other suitable protocols. Serial communication and parallel communication can be bridged by a UART, USART, or other suitable subsystem, as appropriate.

[0127] External communication (including but not limited to software updates, firmware updates, or readings from the ultrasound device) can be accomplished using any suitable wireless or wired communication technology, such as a cable interface (e.g., a USB interface, a Micro USB interface, a Lightning interface, or a FireWire interface), Bluetooth, Wi-Fi, ZigBee, Li-Fi, or a cellular data connection (e.g., 2G / GSM, 3G / UMTS, 4G / LTE / WiMax, or 5G). For example, a Bluetooth Low Energy (BLE) radio can be used to establish a connection with a cloud service for data transfer and to receive software patches. The controller can be configured to communicate with a remote server or a local device (e.g., a laptop, a tablet, or a handheld device), or can include a display that can show status variables and other information. Information can also be transferred on a physical medium (e.g., a USB flash drive or a memory stick).

[0128] In some embodiments, the logic branches can differ from those shown herein. Additional steps can occur, and some of the steps listed herein can not occur. It should also be understood that the described techniques can be used for a variety of types of intraluminal procedures that require intraluminal imaging. Accordingly, logical operations in the embodiments constituting the described techniques are referred to variously as operations, steps, objects, elements, components, or modules. Furthermore, it should be understood that these can occur in any order, unless otherwise explicitly provided or a specific order is inherently necessitated by the disclosure or the claims.

[0129] All directional references (e.g., upper, lower, inner, outer, upward, downward, left, right, lateral, front, back, top, bottom, above, below, vertical, horizontal, clockwise, counterclockwise, proximal, and distal) are only used for identification purposes to aid the reader’s understanding of the requested claims, and do not create limitations, particularly as to the position, orientation, or use of the automatic frame identification system. Connection references (e.g., attached, coupled, connected, and joined) are to be construed broadly and will be given their ordinary and accustomed meaning to an artisan of ordinary skill in the art to which the claimed subject matter applies, unless specifically defined otherwise in the specification. As such, connection references do not necessarily imply that two elements are directly connected to each other without intervening materials between these two elements. The term “or” should be interpreted as meaning “and / or” rather than “exclusive or.” Unless otherwise stated in the claims, recited values are to be construed as being only illustrative and not limiting.

[0130] The above specification, examples and data provide a complete description of the structure and use of exemplary embodiments of the automatic frame identification system defined in the claims. Although the various embodiments of the claimed subject matter have been described above with a certain degree of particularity, one of ordinary skill in the art could make numerous alterations to the details disclosed herein without departing from the spirit or scope of the claimed subject matter.

[0131] Other embodiments are also contemplated. It is intended that all subject matter contained in the above description be interpreted as illustrative only and not as limiting. Changes in detail or structure can be made without departing from the basic elements of the subject matter defined in the claims.

Claims

1. An intraluminal imaging system, comprising: an intraluminal imaging catheter or guidewire configured to obtain imaging data associated with a lumen of a patient when positioned within the lumen; and a processor circuit in communication with the intraluminal imaging catheter or guidewire, the processor circuit configured to: generate a plurality of image frames using the imaging data; identify, from among the plurality of image frames, an image frame that satisfies a first criterion associated with an anatomical feature as a target frame representing a region of interest, wherein the identifying is based on a comparison of an automated measurement of the anatomical feature in the plurality of image frames to a threshold value; perform at least one of (i) and (ii): (i) identify, from among the plurality of image frames, an image frame that satisfies a second criterion associated with the anatomical feature as a candidate proximal reference frame representing proximal-most tissue of the lumen proximal to the target frame, if the candidate proximal reference frame is on a side branch, search distally to find a proximal-most image frame that does not include the side branch and assign the proximal-most image frame as the proximal reference frame, if the candidate proximal reference frame is not on a side branch, assign the candidate proximal reference frame as the proximal reference frame; (ii) identify, from among the plurality of image frames, an image frame that satisfies a third criterion associated with the anatomical feature as a candidate distal reference frame representing distal-most tissue of the lumen distal to the target frame, if the candidate distal reference frame is on a side branch, search proximally to find a distal-most image frame that does not include the side branch and assign the distal-most image frame as the distal reference frame, if the candidate distal reference frame is not on a side branch, assign the candidate distal reference frame as the distal reference frame; and output, to a display in communication with the processor circuit, a single screen display comprising: at least one of the proximal reference frame and the distal reference frame, and the target frame; and a graphical representation of the lumen showing respective locations of at least one of the proximal reference frame and the distal reference frame, and the target frame.

2. The intraluminal imaging system of claim 1, wherein, the first criterion is associated with a plaque burden or cross-sectional area of the lumen.

3. The intraluminal imaging system of claim 2, wherein, the second criterion is associated with a plaque burden or cross-sectional area of the lumen.

4. The intraluminal imaging system of claim 2, wherein, the third criterion is associated with a plaque burden or cross-sectional area of the lumen.

5. The intraluminal imaging system of claim 1, wherein, the processor circuit is configured to define a target region between a plurality of candidate target frames, wherein no reference frames are identified between the plurality of candidate target frames.

6. The intraluminal imaging system of claim 5, wherein, the processor circuit identifying the target frame includes the processor circuit identifying an image frame that satisfies a fourth criterion associated with a plaque burden or cross-sectional area of the plurality of candidate target frames within the target region.

7. The intraluminal imaging system of claim 1, wherein, the processor circuit identifying the target frame includes the processor circuit identifying an image frame representing a desired location at which a stent is to be expanded.

8. The intraluminal imaging system of claim 7, wherein the processor circuit identifying the proximal reference frame includes the processor circuit identifying an image frame representing a desired location of a proximal edge of the stent, and the processor circuit identifying the distal reference frame includes the processor circuit identifying an image frame representing a desired location of a distal edge of the stent. wherein the processor circuit identifying the distal reference frame includes the processor circuit identifying an image frame representing a desired position of a distal edge of the stent.

9. The intraluminal imaging system of claim 1, wherein, the processor circuit identifying the target frame includes the processor circuit identifying an image frame having a smallest cross-sectional area within a stent positioned in the lumen.

10. The intraluminal imaging system of claim 9, wherein the processor circuit is configured to detect a proximal edge and a distal edge of the stent, wherein the processor circuit identifying the proximal reference frame includes the processor circuit identifying an image frame representing the proximal edge of the stent, and wherein the processor circuit identifying the distal reference frame includes the processor circuit identifying an image frame representing the distal edge of the stent.

11. A method for intraluminal imaging, comprising: using a processor circuit in communication with an intraluminal imaging catheter or guidewire, generating a plurality of image frames using imaging data obtained by the intraluminal imaging catheter or guidewire while positioned within a lumen; identifying, from among the plurality of image frames, an image frame satisfying a first criterion associated with an anatomical feature as a target frame representing a region of interest, wherein the identifying is based on a comparison of an automated measurement of the anatomical feature in the plurality of image frames to a threshold value; performing at least one of (i) and (ii): (i) identifying, from among the plurality of image frames, an image frame satisfying a second criterion associated with the anatomical feature as a candidate proximal reference frame representing proximal-most tissue of the lumen proximal to the target frame, if the candidate proximal reference frame is on a side branch, searching distally to find a proximal-most image frame that does not include the side branch, and assigning the proximal-most image frame as the proximal reference frame, if the candidate proximal reference frame is not on a side branch, assigning the candidate proximal reference frame as the proximal reference frame; (ii) identifying, from among the plurality of image frames, an image frame satisfying a third criterion associated with the anatomical feature as a candidate distal reference frame representing proximal-most tissue of the lumen distal to the target frame, if the candidate distal reference frame is on a side branch, searching proximally to find a proximal-most image frame that does not include the side branch, and assigning the proximal-most image frame as the distal reference frame, if the candidate distal reference frame is not on a side branch, assigning the candidate distal reference frame as the distal reference frame; and outputting, to a display in communication with the processor circuit, a single screen display including: at least one of the proximal reference frame and the distal reference frame, and the target frame; and a graphical representation of the lumen showing respective positions of at least one of the proximal reference frame and the distal reference frame, and the target frame.

12. The method of claim 11, wherein, the first criterion is associated with a plaque burden or cross-sectional area of the lumen.

13. The method of claim 12, wherein, the second criterion is associated with a plaque burden or cross-sectional area of the lumen.

14. The method of claim 12, wherein, the third criterion is associated with a plaque burden or cross-sectional area of the lumen.

15. The method of claim 11, further comprising: using the processor circuit to define a target region between a plurality of candidate target frames, wherein no reference frame is identified between the plurality of candidate target frames.

16. The method of claim 15, wherein, identifying the target frame includes identifying an image frame that satisfies a fourth criterion associated with a plaque burden or cross-sectional area of the plurality of candidate target frames within the target region.

17. The method of claim 11, wherein, identifying the target frame includes identifying an image frame that represents a desired location to expand with a stent.

18. The method of claim 17, wherein, identifying the proximal reference frame includes identifying an image frame that represents a desired location of a proximal edge of the stent, and wherein identifying the distal reference frame includes identifying an image frame that represents a desired location of a distal edge of the stent.

19. The method of claim 11, wherein, identifying the target frame includes identifying an image frame that has a minimum cross-sectional area within a stent positioned in the lumen.

20. The method of claim 19, further comprising detecting a proximal edge and a distal edge of the stent, wherein, identifying the proximal reference frame includes identifying an image frame that represents the proximal edge of the stent, and wherein identifying the distal reference frame includes identifying an image frame that represents the distal edge of the stent.

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